Document clustering via dirichlet process mixture model with feature selection

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2010, Pages 763-771

  Yu, Guan ^a   Huang, Ruizhang ^a   Wang, Zhaojun ^b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b NANKAI UNIVERSITY   (China)

Author keywords

Dirichlet process mixture model; Document clustering; Feature selection

Indexed keywords

DATA SETS; DIRICHLET PROCESS MIXTURE MODEL; DOCUMENT CLUSTERING; DOCUMENT COLLECTION; DOCUMENT DATASETS; FEATURE SELECTION; NUMBER OF CLUSTERS; STOCHASTIC SEARCH; VARIABLE SELECTION;

CLUSTER ANALYSIS; INFORMATION RETRIEVAL; MIXTURES; STOCHASTIC MODELS;

FEATURE EXTRACTION;

EID: 77956209448     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835901     Document Type: Conference Paper

References (30)

1. C. Antoniak. (1974). Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics, 2(6):1152-1174.
2. D. Blackwell and J. MacQueen. (1973). Ferguson distribution via Polya urn schemes. The Annals of Statistics, 1(2):353-355.
3. D. Blei and M. Jordan. (2006). Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1(1):121-144.
4. H. Bozdogan. (1983). Determining the number of component clusters in the standard multivariate normal mixture model using model-selection criteria. TR UIC/DQM/A83-1, Quantitative Methods Department, University of Illinois, Chicago, IL.
5. P. J. Brown, M. Vannucci and T. Fearn. (1998). Multivariate Bayesian variable selection and prediction. Journal of the Royal Statistical Society, Series B, 60:627-641.
6. P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freedman. (1988). Autoclass: A Bayesian classification system. In Proceedings of the Fifth International Conference on Machine Learning, pages 54-64.
7. I. S. Dhillon and D. S. Modha. (2001). Concept decompositions for large sparse text data using clustering. Journal of Machine Learning, 42(1):143-175.
8. B. E. Dom. (2001). An information-theoretic external cluster-validity measure. Research Report RJ 10219, IBM.
9. C. Elkan. (2006). Clustering Documents with an Exponential-Family Approximation of the Dirichlet Compound Multinomial Distribution. In Proceedings of the 23th International Conference on Machine Learning, 289-296.
10. T. Ferguson. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1:209-230.
11. C. Fraley and A. E. Raftery. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computer Journal, 41(8):578-588.
12. E. I. George and R. E. McCulloch. (1992). Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88:881-889.
13. P. J. Green and S. M. Richardson. (2001). Modelling Heterogeneity with and without the Dirichlet Process. Scandinavian Journal of Statistics, 28:355-377.
14. J. Ishwaran and L. James. (2001). Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 96:161-174.
15. H. Ishwaran and M. Zarepour. (2002). Exact and Approximate Sum-Representations for the Dirichlet process. Canadian Journal of Statistics, 30:269-283.
16. S. Kim. (2006). Variable selection in clustering via Dirichlet process mixture models. Biometrika, 93(4):877-893.
17. M. H. C. Law, M. A. T. Figueiredo, and A. K. Jain. (2004). Simultaneous feature selection and clustering using mixture models. IEEE Trans. Pattern Analysis and Machine Intelligence, 26(9):1154-1166.
18. J. MacQueen. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 281-297.
19. R. Madsen, D. Kauchak, and C. Elkan. (2005). Modeling word burstness using the Dirichlet distribution. In Proceedings of the 22th International Conference on Machine Learning, 545-552.
20. R. Neal. (1992). Bayesian mixture modeling. In Proceedings of the Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis, 11:197-211.
21. R. Neal. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9(2):249-265.
22. K. Nigam, A. K. McCallum, S. Thrun, and T. M. Mitchel. (2000). Text classification from labeled and unlabeled documents using EM. Journal of Machine Learning, 39(2/3):103-134.
23. J. Rissanen. (1978). Modeling by shortest data description. Automatica, 14:465-471.
24. K. Rose. (1998). Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. In Proceedings of the IEEE, 86(11):2210-2239.
25. G. Schwarz. (1978). Estimating the dimension of a model. The Annals of Statistics, 6:461-464.
26. Z. Shi. (2006). Semi-supervised model-based document clustering: A comparative study. Journal of Machine Learning, 65(1):3-29.
27. P. Smyth. (1998). Model selection for probabilistic clustering using cross-validated likelihood. ICS Tech Report 98-09, Statistics and Computing.
28. Y. W. Teh, M. I. Jordan, M.J. Beal, and D.M. Blei. (2007). Hierarchical Dirichlet Processes. Journal of the American Statistical Association, 101(476):1566-1581.
29. A. Vlachos, Z. Ghahramani, and A. Korhonen. (2008). Dirichlet process mixture models for verb clustering. ICML Workshop on Prior Knowledge for Text and Language Processing, Helsinki, Finland.
30. C. Wallace and P. Freedman. (1987). Estimation and inference by compact coding. Journal of the Royal Statistical Society, Series B, 49(3):240-265.